Use NCCL instead of ray for control-plane communication to remove serialization overhead (#2221)

vllm/model_executor/models/internlm.py

@@ -255,7 +255,7 @@ class InternLMForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/llama.py

@@ -291,7 +291,7 @@ class LlamaForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/mistral.py

@@ -287,7 +287,7 @@ class MistralForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/mixtral.py

@@ -320,7 +320,7 @@ class MixtralModel(nn.Module):
         positions: torch.Tensor,
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
-    ) -> SamplerOutput:
+    ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for i in range(len(self.layers)):
@@ -361,7 +361,7 @@ class MixtralForCausalLM(nn.Module):
         self,
         hidden_states: Optional[torch.Tensor],
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/mpt.py

@@ -276,7 +276,7 @@ class MPTForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/opt.py

@@ -309,7 +309,7 @@ class OPTForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/phi_1_5.py

@@ -280,7 +280,7 @@ class PhiForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         head = self.lm_head.linear
         next_tokens = self.sampler(head.weight, hidden_states,
                                    sampling_metadata, head.bias)

vllm/model_executor/models/qwen.py

@@ -247,7 +247,7 @@ class QWenLMHeadModel(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/models/yi.py

@@ -286,7 +286,7 @@ class YiForCausalLM(nn.Module):
         self,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> SamplerOutput:
+    ) -> Optional[SamplerOutput]:
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    sampling_metadata)
         return next_tokens

vllm/model_executor/parallel_utils/communication_op.py

 import torch
 
 from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
     get_tensor_model_parallel_group,
 )
@@ -45,3 +46,61 @@ def tensor_model_parallel_all_gather(input_, dim=-1):
                                           (world_size * input_size[dim], ) +
                                           input_size[dim + 1:])
     return output_tensor
+
+
+def tensor_model_parallel_gather(input_, dst=0, dim=-1):
+    """Gather the input tensor across model parallel group.
+
+    NOTE: We assume that the input tensor is on the same device across
+    all the ranks.
+    """
+    world_size = get_tensor_model_parallel_world_size()
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+    assert -input_.dim() <= dim < input_.dim(), (
+        f"Invalid dim ({dim}) for input tensor with shape {input_.size()}")
+    if dim < 0:
+        # Convert negative dim to positive.
+        dim += input_.dim()
+    # Allocate output tensor.
+    if get_tensor_model_parallel_rank() == dst:
+        gather_list = [torch.empty_like(input_) for _ in range(world_size)]
+    else:
+        gather_list = None
+    # Gather.
+    torch.distributed.gather(input_,
+                             gather_list,
+                             dst=dst,
+                             group=get_tensor_model_parallel_group())
+    if get_tensor_model_parallel_rank() == dst:
+        output_tensor = torch.cat(gather_list, dim=dim)
+    else:
+        output_tensor = None
+    return output_tensor
+
+
+def broadcast(input_, src=0):
+    """Broadcast the input tensor."""
+    world_size = torch.distributed.get_world_size()
+    assert 0 <= src < world_size, f"Invalid src rank ({src})"
+
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input_
+    # Broadcast.
+    torch.distributed.broadcast(input_, src=src)
+    return input_
+
+
+def broadcast_object_list(obj_list, src=0):
+    """Broadcast the input object list."""
+    world_size = torch.distributed.get_world_size()
+    assert 0 <= src < world_size, f"Invalid src rank ({src})"
+
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return obj_list
+    # Broadcast.
+    torch.distributed.broadcast_object_list(obj_list, src=src)
+    return obj_list

vllm/model_executor/sampling_metadata.py

 from dataclasses import dataclass
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 
@@ -18,24 +18,29 @@ class SamplingMetadata:
         seq_data: Seq_id -> SequenceData.
         prompt_lens: Lengths of prompts.
         selected_token_indices: Token indices selected for sampling.
-        categorized_sample_indices: SamplingType -> token indicies to sample.
+        categorized_sample_indices: SamplingType -> token indices to sample.
+        perform_sampling: Whether to perform sampling. This option is used to
+            make the sampling only happens in the driver worker, and disable
+            sampling in other worker processes.
     """
 
     def __init__(
         self,
-        seq_groups: List[Tuple[List[int], SamplingParams]],
-        seq_data: Dict[int, SequenceData],
-        prompt_lens: List[int],
+        seq_groups: Optional[List[Tuple[List[int], SamplingParams]]],
+        seq_data: Optional[Dict[int, SequenceData]],
+        prompt_lens: Optional[List[int]],
         selected_token_indices: torch.Tensor,
-        categorized_sample_indices: Dict[SamplingType, torch.Tensor],
+        categorized_sample_indices: Optional[Dict[SamplingType, torch.Tensor]],
+        perform_sampling: bool = True,
     ) -> None:
         self.seq_groups = seq_groups
         self.seq_data = seq_data
         self.prompt_lens = prompt_lens
         self.selected_token_indices = selected_token_indices
         self.categorized_sample_indices = categorized_sample_indices
+        self.perform_sampling = perform_sampling
 
-        self.num_prompts = len(prompt_lens)
+        self.num_prompts = len(prompt_lens) if prompt_lens is not None else 0
 
     def __repr__(self) -> str:
         return (
@@ -44,7 +49,8 @@ class SamplingMetadata:
             f"seq_data={self.seq_data}, "
             f"prompt_lens={self.prompt_lens}, "
             f"selected_token_indices={self.selected_token_indices}, "
-            f"categorized_sample_indices={self.categorized_sample_indices})")
+            f"categorized_sample_indices={self.categorized_sample_indices}), "
+            f"perform_sampling={self.perform_sampling})")
 
 
 @dataclass

vllm/utils.py

 import enum
+import os
 import socket
 import uuid
 from platform import uname
+from typing import List
 
 import psutil
 import torch
@@ -55,7 +57,15 @@ def in_wsl() -> bool:
     return "microsoft" in " ".join(uname()).lower()
 
 
-def get_open_port():
+def get_ip() -> str:
+    return socket.gethostbyname(socket.gethostname())
+
+
+def get_open_port() -> int:
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
         s.bind(("", 0))
         return s.getsockname()[1]
+
+
+def set_cuda_visible_devices(device_ids: List[int]) -> None:
+    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, device_ids))

vllm/worker/model_runner.py

 import time
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -8,6 +8,8 @@ import torch.nn as nn
 from vllm.config import ModelConfig, ParallelConfig, SchedulerConfig
 from vllm.logger import init_logger
 from vllm.model_executor import get_model, InputMetadata, SamplingMetadata
+from vllm.model_executor.parallel_utils.communication_op import (
+    broadcast, broadcast_object_list)
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
 from vllm.utils import in_wsl
@@ -28,10 +30,12 @@ class ModelRunner:
         model_config: ModelConfig,
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
+        is_driver_worker: bool = False,
     ):
         self.model_config = model_config
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
+        self.is_driver_worker = is_driver_worker
 
         # model_config can be None in tests/samplers/test_sampler.py.
         # FIXME(woosuk): This is a hack to make the tests work. Refactor this.
@@ -70,7 +74,7 @@ class ModelRunner:
     def _prepare_prompt(
         self,
         seq_group_metadata_list: List[SequenceGroupMetadata],
-    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, List[int]]:
         assert len(seq_group_metadata_list) > 0
         input_tokens: List[List[int]] = []
         input_positions: List[List[int]] = []
@@ -135,14 +139,14 @@ class ModelRunner:
                                              dtype=torch.long)
 
         input_metadata = InputMetadata(
-            prompt_lens=prompt_lens,
+            is_prompt=True,
             slot_mapping=slot_mapping,
             max_context_len=None,
             context_lens=None,
             block_tables=None,
             use_cuda_graph=False,
         )
-        return input_tokens, input_positions, input_metadata
+        return input_tokens, input_positions, input_metadata, prompt_lens
 
     def _prepare_decode(
         self,
@@ -203,32 +207,24 @@ class ModelRunner:
                 block_tables.append([])
             batch_size = graph_batch_size
 
-        # When using CUDA graph, we don't need to make the tensors on the GPU
-        # because they will be eventually copied to the designated GPU buffer.
-        device = "cpu" if use_captured_graph else "cuda"
-        pin_memory = use_captured_graph and not self.in_wsl
         input_tokens = _make_tensor_with_pad(input_tokens,
                                              max_len=1,
                                              pad=0,
                                              dtype=torch.long,
-                                             device=device,
-                                             pin_memory=pin_memory)
+                                             device="cuda")
         input_positions = _make_tensor_with_pad(input_positions,
                                                 max_len=1,
                                                 pad=0,
                                                 dtype=torch.long,
-                                                device=device,
-                                                pin_memory=pin_memory)
+                                                device="cuda")
         slot_mapping = _make_tensor_with_pad(slot_mapping,
                                              max_len=1,
                                              pad=_PAD_SLOT_ID,
                                              dtype=torch.long,
-                                             device=device,
-                                             pin_memory=pin_memory)
+                                             device="cuda")
         context_lens = torch.tensor(context_lens,
                                     dtype=torch.int,
-                                    device=device,
-                                    pin_memory=pin_memory)
+                                    device="cuda")
 
         if use_captured_graph:
             # The shape of graph_block_tables is
@@ -237,17 +233,18 @@ class ModelRunner:
             for i, block_table in enumerate(block_tables):
                 if block_table:
                     input_block_tables[i, :len(block_table)] = block_table
-            block_tables = torch.tensor(input_block_tables, device=device)
+            block_tables = torch.tensor(input_block_tables, device="cuda")
         else:
             block_tables = _make_tensor_with_pad(
                 block_tables,
                 max_len=max_context_len,
                 pad=0,
                 dtype=torch.int,
+                device="cuda",
             )
 
         input_metadata = InputMetadata(
-            prompt_lens=[],
+            is_prompt=False,
             slot_mapping=slot_mapping,
             max_context_len=max_context_len,
             context_lens=context_lens,
@@ -326,23 +323,127 @@ class ModelRunner:
         )
         return sampling_metadata
 
+    def prepare_input_tensors(
+        self,
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, SamplingMetadata]:
+        if self.is_driver_worker:
+            # NOTE: We assume that all sequences in the group are all prompts or
+            # all decodes.
+            is_prompt = seq_group_metadata_list[0].is_prompt
+            # Prepare input tensors.
+            if is_prompt:
+                (input_tokens, input_positions, input_metadata,
+                 prompt_lens) = self._prepare_prompt(seq_group_metadata_list)
+            else:
+                (input_tokens, input_positions, input_metadata
+                 ) = self._prepare_decode(seq_group_metadata_list)
+                prompt_lens = []
+            sampling_metadata = self._prepare_sample(seq_group_metadata_list,
+                                                     prompt_lens)
+
+            def get_size_or_none(x: Optional[torch.Tensor]):
+                return x.size() if x is not None else None
+
+            # Broadcast the input data. For input tensors, we first broadcast
+            # its shape and then broadcast the tensor to avoid high
+            # serialization cost.
+            py_data = {
+                "input_tokens_size":
+                input_tokens.size(),
+                "input_positions_size":
+                input_positions.size(),
+                "is_prompt":
+                input_metadata.is_prompt,
+                "slot_mapping_size":
+                get_size_or_none(input_metadata.slot_mapping),
+                "max_context_len":
+                input_metadata.max_context_len,
+                "context_lens_size":
+                get_size_or_none(input_metadata.context_lens),
+                "block_tables_size":
+                get_size_or_none(input_metadata.block_tables),
+                "use_cuda_graph":
+                input_metadata.use_cuda_graph,
+                "selected_token_indices_size":
+                sampling_metadata.selected_token_indices.size(),
+            }
+            broadcast_object_list([py_data], src=0)
+            # TODO(zhuohan): Combine the broadcasts or set async_op=True.
+            broadcast(input_tokens, src=0)
+            broadcast(input_positions, src=0)
+            if input_metadata.slot_mapping is not None:
+                broadcast(input_metadata.slot_mapping, src=0)
+            if input_metadata.context_lens is not None:
+                broadcast(input_metadata.context_lens, src=0)
+            if input_metadata.block_tables is not None:
+                broadcast(input_metadata.block_tables, src=0)
+            broadcast(sampling_metadata.selected_token_indices, src=0)
+        else:
+            receving_list = [None]
+            broadcast_object_list(receving_list, src=0)
+            py_data = receving_list[0]
+            input_tokens = torch.empty(*py_data["input_tokens_size"],
+                                       dtype=torch.long,
+                                       device="cuda")
+            broadcast(input_tokens, src=0)
+            input_positions = torch.empty(*py_data["input_positions_size"],
+                                          dtype=torch.long,
+                                          device="cuda")
+            broadcast(input_positions, src=0)
+            if py_data["slot_mapping_size"] is not None:
+                slot_mapping = torch.empty(*py_data["slot_mapping_size"],
+                                           dtype=torch.long,
+                                           device="cuda")
+                broadcast(slot_mapping, src=0)
+            else:
+                slot_mapping = None
+            if py_data["context_lens_size"] is not None:
+                context_lens = torch.empty(*py_data["context_lens_size"],
+                                           dtype=torch.int,
+                                           device="cuda")
+                broadcast(context_lens, src=0)
+            else:
+                context_lens = None
+            if py_data["block_tables_size"] is not None:
+                block_tables = torch.empty(*py_data["block_tables_size"],
+                                           dtype=torch.int,
+                                           device="cuda")
+                broadcast(block_tables, src=0)
+            else:
+                block_tables = None
+            selected_token_indices = torch.empty(
+                *py_data["selected_token_indices_size"],
+                dtype=torch.long,
+                device="cuda")
+            broadcast(selected_token_indices, src=0)
+            input_metadata = InputMetadata(
+                is_prompt=py_data["is_prompt"],
+                slot_mapping=slot_mapping,
+                max_context_len=py_data["max_context_len"],
+                context_lens=context_lens,
+                block_tables=block_tables,
+                use_cuda_graph=py_data["use_cuda_graph"],
+            )
+            sampling_metadata = SamplingMetadata(
+                seq_groups=None,
+                seq_data=None,
+                prompt_lens=None,
+                selected_token_indices=selected_token_indices,
+                categorized_sample_indices=None,
+                perform_sampling=False,
+            )
+
+        return input_tokens, input_positions, input_metadata, sampling_metadata
+
     @torch.inference_mode()
     def execute_model(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
         kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
-    ) -> SamplerOutput:
-        # NOTE: We assume that all sequences in the group are all prompts or
-        # all decodes.
-        is_prompt = seq_group_metadata_list[0].is_prompt
-        # Prepare input tensors.
-        if is_prompt:
-            inputs = self._prepare_prompt(seq_group_metadata_list)
-            input_tokens, input_positions, input_metadata = inputs
-        else:
-            inputs = self._prepare_decode(seq_group_metadata_list)
-            input_tokens, input_positions, input_metadata = inputs
-
+    ) -> Optional[SamplerOutput]:
+        input_tokens, input_positions, input_metadata, sampling_metadata = (
+            self.prepare_input_tensors(seq_group_metadata_list))
         # Execute the model.
         if input_metadata.use_cuda_graph:
             graph_batch_size = input_tokens.shape[0]
@@ -356,9 +457,6 @@ class ModelRunner:
             input_metadata=input_metadata,
         )
 
-        sampling_metadata = self._prepare_sample(seq_group_metadata_list,
-                                                 input_metadata.prompt_lens)
-
         # Sample the next token.
         output = self.model.sample(
             hidden_states=hidden_states,
@@ -424,7 +522,7 @@ class ModelRunner:
         for batch_size in reversed(_BATCH_SIZES_TO_CAPTURE):
             # Create dummy input_metadata.
             input_metadata = InputMetadata(
-                prompt_lens=[],
+                is_prompt=False,
                 slot_mapping=slot_mapping[:batch_size],
                 max_context_len=self.max_context_len_to_capture,
                 context_lens=context_lens[:batch_size],

vllm/worker/worker.py

@@ -8,6 +8,8 @@ import torch.distributed
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig)
 from vllm.model_executor import set_random_seed
+from vllm.model_executor.parallel_utils.communication_op import (
+    broadcast_object_list)
 from vllm.model_executor.parallel_utils.parallel_state import (
     initialize_model_parallel)
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
@@ -28,17 +30,23 @@ class Worker:
         model_config: ModelConfig,
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
-        rank: Optional[int] = None,
-        distributed_init_method: Optional[str] = None,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
     ) -> None:
         self.model_config = model_config
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
+        self.local_rank = local_rank
         self.rank = rank
         self.distributed_init_method = distributed_init_method
+        self.is_driver_worker = is_driver_worker
+        if self.is_driver_worker:
+            assert self.rank == 0, "The driver worker must have rank 0."
 
         self.model_runner = ModelRunner(model_config, parallel_config,
-                                        scheduler_config)
+                                        scheduler_config, is_driver_worker)
         # Uninitialized cache engine. Will be initialized by
         # self.init_cache_engine().
         self.cache_config = None
@@ -57,13 +65,7 @@ class Worker:
 
         # This env var set by Ray causes exceptions with graph building.
         os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
-        # Env vars will be set by Ray.
-        self.rank = self.rank if self.rank is not None else int(
-            os.getenv("RANK", "-1"))
-        local_rank = int(os.getenv("LOCAL_RANK", "0"))
-        self.device = torch.device(f"cuda:{local_rank}")
-        if self.rank < 0:
-            raise ValueError("Invalid or unspecified rank.")
+        self.device = torch.device(f"cuda:{self.local_rank}")
         torch.cuda.set_device(self.device)
 
         _check_if_gpu_supports_dtype(self.model_config.dtype)
@@ -125,14 +127,12 @@ class Worker:
         # the model initialization and profiling.
         set_random_seed(self.model_config.seed)
 
-    @torch.inference_mode()
-    def execute_model(
+    def cache_swap(
         self,
-        seq_group_metadata_list: List[SequenceGroupMetadata],
         blocks_to_swap_in: Dict[int, int],
         blocks_to_swap_out: Dict[int, int],
         blocks_to_copy: Dict[int, List[int]],
-    ) -> SamplerOutput:
+    ) -> None:
         # Issue cache operations.
         issued_cache_op = False
         if blocks_to_swap_in:
@@ -152,8 +152,38 @@ class Worker:
         if cache_events is not None:
             for event in cache_events:
                 event.wait()
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None,
+        blocks_to_swap_in: Optional[Dict[int, int]] = None,
+        blocks_to_swap_out: Optional[Dict[int, int]] = None,
+        blocks_to_copy: Optional[Dict[int, List[int]]] = None,
+    ) -> Optional[SamplerOutput]:
+        if self.is_driver_worker:
+            assert seq_group_metadata_list is not None
+            num_seq_groups = len(seq_group_metadata_list)
+            assert blocks_to_swap_in is not None
+            assert blocks_to_swap_out is not None
+            assert blocks_to_copy is not None
+            block_swapping_info = [
+                blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy
+            ]
+            broadcast_object_list([num_seq_groups] + block_swapping_info,
+                                  src=0)
+        else:
+            # num_seq_groups, blocks_to_swap_in, blocks_to_swap_out,
+            # blocks_to_copy (4 elements)
+            recv_data = [None] * 4
+            broadcast_object_list(recv_data, src=0)
+            num_seq_groups = recv_data[0]
+            block_swapping_info = recv_data[1:]
+
+        self.cache_swap(*block_swapping_info)
+
         # If there is no input, we don't need to execute the model.
-        if not seq_group_metadata_list:
+        if num_seq_groups == 0:
             return {}
 
         output = self.model_runner.execute_model(seq_group_metadata_list,